[250420] New survey paper on Deep Generative Models in Transportation Research published in Transportation Research Part C
๐ Itโs officialโour new paper is live!
Iโm thrilled to share that our latest publication, โ๐ ๐๐๐ง๐ญ๐ฅ๐ ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐๐ฎ๐ญ๐จ๐ซ๐ข๐๐ฅ ๐จ๐ง ๐๐๐๐ฉ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐ฏ๐ ๐๐จ๐๐๐ฅ๐ฌ ๐ข๐ง ๐๐ซ๐๐ง๐ฌ๐ฉ๐จ๐ซ๐ญ๐๐ญ๐ข๐จ๐ง ๐๐๐ฌ๐๐๐ซ๐๐กโ, has just been published in Transportation Research Part C. This work is a true team effort from experts around the world, including Zhixiong Jin, Seung Woo Ham, Jiwon Kim, Lijun Sun, and me. We hope this paper serves as a go-to primer on Generative AI for transportation researchers.
In this paper, we provide an accessible overview of Deep Generative Models (DGMs) and their applications for transportation research communities. Our paper offers a comprehensive introduction to the foundational concepts of DGMs and a review of current literature on applications of DGMs in transportation research.
To further support the transportation research community, we have included open-source tutorial code to help researchers apply DGMs to a variety of transportation problems, featuring two widely studied problems to assist a broad range of researchers:
- Generating Household Travel Survey data using the National Household Travel Survey in Korea, and
- Generating highway traffic speed contour using I-24 MOTION data
๐ Free access until July 4, 2025:
๐ป Tutorial Code repo: